Evaluating device quality

ABSTRACT

Systems and methods for evaluating device quality are disclosed. Some implementations include receiving, operational parameters for a mobile device, where the operational parameters include data related to service disturbances in a plurality of areas of operational performance of the mobile device, computing, for each dimension of a plurality of dimensions, a curve indicative of a relationship between a device quality index (DQI) and operational parameters of the mobile device, where each dimension corresponds to a particular area of operational performance of the mobile device, identifying cliff points in each dimension of a multi-dimensional surface fitted to each computed curve, where each cliff point corresponds to a reduction in a user&#39;s quality of experience in a particular area of operational performance, and projecting the identified cliff points in each dimension of the multi-dimensional surface to determine a horizontal space in the multi-dimensional surface corresponding to a region of acceptable DQIs.

CROSS REFERENCE TO RELATED APPLICATION

This application is related to U.S. patent application Ser. No. ______,having Attorney Docket Number 20131360 (050108-0817), entitled“FORECASTING DEVICE RETURN RATE,” being filed concurrently herewith, theentire content of which is incorporated herein by reference.

BACKGROUND

In recent years, mobile device usage has significantly increased. Mobiledevices, such as smartphones, are being designed and manufactured at arapid rate to satisfy customer demand. Companies manufacturing mobiledevices, or wireless network providers marketing the mobile devices, mayuse performance measurement to evaluate device quality. A keyperformance indicator (KPI) is a type of performance measurement. Anorganization may use KPIs to evaluate quality of a device. One approachto evaluate a device's quality is to manually test device KPIs using aquality testing tool. Then, if certain tested KPIs pass respectiveacceptable KPI values, the device is determined to have passed a qualitytest. Because each KPI may need to be separately evaluated, such methodsmay not be efficient. More importantly, such methods may not take intoconsideration all features indicative of a device's quality during aperformance test. A combined impact from various device KPIs on devicequality are also overlooked.

Thus, the resulting performance test may not be comprehensive.Furthermore, such methods are limited to evaluating device quality formobile devices that have already been launched and are presently in useby customers.

As the foregoing illustrates, a new approach for evaluating devicequality may be desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing figures depict one or more implementations in accord withthe present teachings, by way of example only, not by way of limitation.In the figures, like reference numerals refer to the same or similarelements.

FIG. 1 is a high-level functional block diagram of an example of asystem of networks/devices that provide various communications formobile stations and support an example of evaluating device quality.

FIG. 2 is an exemplary overall framework that can be used to obtain andstore operational parameters related to a mobile device.

FIG. 3 is an exemplary incremental learning in an analytics engine.

FIG. 4 is an exemplary interface to define relations between dataattributes in metadata.

FIG. 5 is exemplary data sources that can provide data to anextract-transform-load (ETL) module and the analytics engine.

FIG. 6 is an exemplary relationship between a device quality index (DQI)and service disturbance for an application.

FIGS. 7A and 7B are exponential relationships between DQI and servicequality disturbances.

FIG. 8 is an exemplary cliff model in accordance with theimplementations.

FIG. 9 is a high-level functional block diagram of an exemplarynon-touch type mobile station that can be associated with a devicequality evaluation service through a network/system like that shown inFIG. 1.

FIG. 10 is a high-level functional block diagram of an exemplary touchscreen type mobile station that can be evaluated by the device qualityevaluation service through a network/system like that shown in FIG. 1.

FIG. 11 is a simplified functional block diagram of a computer that maybe configured as a host or server, for example, to function as theanalytics engine in the system of FIG. 1.

FIG. 12 is a simplified functional block diagram of a personal computeror other work station or terminal device.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The various implementations disclosed herein relate to evaluating devicequality. The implementations provide a unified model and framework toevaluate device quality and can further provide a report describing theevaluated device quality. The report may include parameters consideredin evaluating the device quality together with an indication of thedevice quality relative to pre-determined (or acceptable) qualitylevels. For example, the report may include an indication of whether thequality of the device is above-par, sub-par or on-par relative topre-determined (or acceptable) quality levels. The report may bedisplayed on a user interface of a computing device. The implementationscan jointly incorporate independent device performance indicators in theunified model to comprehensively evaluate device performance. Theimplementations can be used to estimate device quality for bothpre-launch and post-launch devices regardless of which originalequipment manufacturer (OEM) manufactured the devices.

In some implementations, one or more operational parameters for a mobiledevice are received at an analytics engine. The operational parameterscan include data related to service disturbances in a plurality of areasof operational performance of the mobile device. The analytics enginecan compute, for each dimension of a plurality of dimensions, a curveindicative of a relationship between a device quality index (DQI) of themobile device for a particular dimension and operational parameters ofthe mobile device for the particular dimension. Each dimension of theplurality of dimensions can correspond to a particular area ofoperational performance of the mobile device.

The analytics engine then identifies cliff points in each dimension of amulti-dimensional surface fitted to each computed curve. Each cliffpoint corresponds to a reduction in a user's quality of experience in aparticular area of operational performance represented by acorresponding dimension. The reduction may be associated with theservice disturbances. The analytics engine then projects the identifiedcliff points in each dimension of the multi-dimensional surface todetermine a horizontal space in the multi-dimensional surfacecorresponding to a region of acceptable DQIs. The analytics engine mayreceive a DQI associated with the mobile device and determining whetherthe received DQI is within the region of the acceptable DQIs. When thereceived DQI is determined to be the region of the acceptable DQIs, theanalytics engine may determine that the mobile device is of acceptablequality. The analytics engine may generate, based on the projection, adevice quality evaluation report for the plurality of areas ofoperational performance of the mobile device. In this way, for example,the disclosed implementations may evaluate device quality.

Reference now is made in detail to the examples illustrated in theaccompanying drawings and discussed below.

FIG. 1 illustrates a system 10 offering a variety of mobilecommunication services, including communications for evaluation ofdevice quality. The example shows simply two mobile stations (MSs) 13 aand 13 b as well as a mobile communication network 15. The stations 13 aand 13 b are examples of mobile stations that can be evaluated by thedevice quality evaluation service. However, the network will providesimilar communications for many other similar users as well as formobile devices/users that do not participate in the device qualityevaluation service. The network 15 provides mobile wirelesscommunications services to those stations as well as to other mobilestations (not shown), for example, via a number of base stations (BSs)17. The present techniques may be implemented in any of a variety ofavailable mobile networks 15 and/or on any type of mobile stationcompatible with such a network 15, and the drawing shows only a verysimplified example of a few relevant elements of the network 15 forpurposes of discussion here.

The wireless mobile communication network 15 might be implemented as anetwork conforming to the code division multiple access (CDMA) IS-95standard, the 3rd Generation Partnership Project 2 (3GPP2) wireless IPnetwork standard including the Global System for Mobile (GSM)communication standard, a Long Term Evolution (LTE) standard or theEvolution Data Optimized (EVDO) standard, a time division multipleaccess (TDMA) standard or other standards used for public mobilewireless communications. The mobile stations 13 can be capable of voicetelephone communications through the network 15, and communications thatmay be needed to evaluate device quality, the exemplary devices 13 a and13 b are capable of data communications through the particular type ofnetwork 15 (and the users thereof typically will have subscribed to dataservice through the network).

The network 15 allows users of the mobile stations such as 13 a and 13 b(and other mobile stations not shown) to initiate and receive telephonecalls to each other as well as through the public switched telephonenetwork or “PSTN” 19 and telephone stations 21 connected to the PSTN.The network 15 typically offers a variety of data services via theInternet 23, such as downloads, web browsing, email, etc. By way ofexample, the drawing shows a laptop PC type user terminal 27 as well asa server 25 connected to the Internet 23; and the data services for themobile stations 13 via the Internet 23 may be with devices like thoseshown at 25 and 27 as well as with a variety of other types of devicesor systems capable of data communications through various interconnectednetworks. The mobile stations 13 a and 13 b also can receive and executeapplications written in various programming languages, as discussed morelater.

Mobile stations 13 can take the form of portable handsets, tablets,smart-phones or personal digital assistants, although they may beimplemented in other form factors. Program applications, including anyapplication to assist in the device quality evaluation service can beconfigured to execute on many different types of mobile stations 13. Forexample, a mobile station application can be written to execute on abinary runtime environment for mobile (BREW-based) mobile station, aWindows Mobile based mobile station, Android, I-Phone, Java Mobile, orRIM based mobile station such as a BlackBerry or the like. Some of thesetypes of devices can employ a multi-tasking operating system.

The mobile communication network 10 can be implemented by a number ofinterconnected networks. Hence, the overall network 10 may include anumber of radio access networks (RANs), as well as regional groundnetworks interconnecting a number of RANs and a wide area network (WAN)interconnecting the regional ground networks to core network elements. Aregional portion of the network 10, such as that serving mobile stations13, can include one or more RANs and a regional circuit and/or packetswitched network and associated signaling network facilities.

Physical elements of a RAN operated by one of the mobile serviceproviders or carriers, include a number of base stations represented inthe example by the base stations (BSs) 17. Although not separatelyshown, such a base station 17 can include a base transceiver system(BTS), which can communicate via an antennae system at the site of basestation and over the airlink with one or more of the mobile stations 13,when the mobile stations are within range. Each base station can includea BTS coupled to several antennae mounted on a radio tower within acoverage area often referred to as a “cell.” The BTS is the part of theradio network that sends and receives RF signals to/from the mobilestations 13 that are served by the base station 17.

The radio access networks can also include a traffic network representedgenerally by the cloud at 15, which carries the user communications anddata for the mobile stations 13 between the base stations 17 and otherelements with or through which the mobile stations communicate. Thenetwork can also include other elements that support functionality otherthan device-to-device media transfer services such as messaging servicemessages and voice communications. Specific elements of the network 15for carrying the voice and data traffic and for controlling variousaspects of the calls or sessions through the network 15 are omitted hereform simplicity. It will be understood that the various network elementscan communicate with each other and other aspects of the mobilecommunications network 10 and other networks (e.g., the public switchedtelephone network (PSTN) and the Internet) either directly orindirectly.

The carrier will also operate a number of systems that provide ancillaryfunctions in support of the communications services and/or applicationservices provided through the network 10, and those elements communicatewith other nodes or elements of the network 10 via one or more privateIP type packet data networks 29 (sometimes referred to as an Intranet),i.e., a private networks. Generally, such systems are part of orconnected for communication via the private network 29. A person skilledin the art, however, would recognize that systems outside of the privatenetwork could serve the same functions as well. Examples of suchsystems, in this case operated by the network service provider as partof the overall network 10, which communicate through the intranet typenetwork 29, include one or more analytics engine(s) 31 and a relatedauthentication server 33 for the application service of server 31. Insome implementations, analytics engine 31 may be an application serverconfigured to execute any type of software application.

A mobile station 13 communicates over the air with a base station 17 andthrough the traffic network 15 for various voice and datacommunications, e.g. through the Internet 23 with a server 25 and/orwith analytics engine 31. If the mobile service carrier offers thedevice quality evaluation service, the service may be hosted on acarrier operated analytics engine 31, for communication via the networks15 and 29. Alternatively, the device quality evaluation service may beprovided by a separate entity (alone or through agreements with thecarrier), in which case, the service may be hosted on an applicationserver such as server 25 connected for communication via the networks 15and 23. Servers such as 25 and 31 may provide any of a variety of commonapplication or service functions in support of or in addition to anapplication program running on the mobile station 13. However, forpurposes of further discussion, we will focus on functions thereof insupport of the device quality evaluation service. For a given service,including the device quality evaluation service, an application programwithin the mobile station may be considered as a ‘client’ and theprogramming at 25 or 31 may be considered as the ‘server’ applicationfor the particular service. In some implementations, the device qualityevaluation service may operate entirely at server 25 or 31.

To insure that the application service offered by analytics engine 31 isavailable to only authorized devices/users, the provider of theapplication service also deploys an authentication server 33. Theauthentication server 33 could be a separate physical server as shown,or authentication server 33 could be implemented as another programmodule running on the same hardware platform as the analytics engine 31.Essentially, when the server application (analytics engine 31 in ourexample) receives a service/data request from a client application on amobile station 13, the server application provides appropriateinformation to the authentication server 33 to allow the authenticationapplication 33 to authenticate the mobile station 13 as outlined herein.Upon successful authentication, the server 33 informs the analyticsengine 31, which in turn provides access to the service via datacommunication through the various communication elements (e.g. 29, 15and 17) of the network 10. A similar authentication function may beprovided for the device quality evaluation service offered via theserver 25, either by the server 33 if there is an appropriatearrangement between the carrier and the operator of server 25, by aprogram on the server 25 or via a separate authentication server (notshown) connected to the Internet 23.

In some implementations, one or more operational parameters for mobilestation 13 a are received at the analytics engine 31. The operationalparameters can include data related to service disturbances in aplurality of areas of operational performance of the mobile station 13a. The analytics engine 31 can compute, for each dimension of aplurality of dimensions, a curve indicative of a non-linear relationshipbetween a DQI of the mobile station 13 a for a particular dimension andoperational parameters of the mobile station 13 a for the particulardimension. Each dimension of the plurality of dimensions can correspondto a particular area of operational performance (e.g., networkperformance, browser performance, signalling performance, etc.) of themobile station 13 a. The analytics engine 31 then identifies cliffpoints in each dimension of a multi-dimensional surface fitted to eachcomputed curve. Each cliff point corresponds to a reduction in a user'squality of experience in a particular area of operational performancerepresented by a corresponding dimension. The reduction may beassociated with the service disturbances which can be represented byservice quality indicators. The analytics engine 31 then projects theidentified cliff points in each dimension of the multi-dimensionalsurface to determine a horizontal space in the multi-dimensional surfacecorresponding to a region of acceptable DQIs. The analytics engine 31may receive a DQI associated with the mobile station 13 a anddetermining whether the received DQI is within the region of theacceptable DQIs. When the received DQI is determined to be the region ofthe acceptable DQIs, the analytics engine 31 may determine that themobile station 13 a is of acceptable quality. When the received DQI isnot determined to be the region of the acceptable DQIs, the analyticsengine 31 may determine that the mobile station 13 a may not be ofacceptable quality. In this way, for example, the disclosedimplementations may evaluate device quality (e.g., quality of mobiledevice 13 a). Exemplary computations by the analytics engine 31 arediscussed further and, for example, with reference to FIG. 8.

FIG. 2 illustrates an exemplary overall framework 200 that can be usedto obtain and store operational parameters related to the mobile station13 a. The overall framework 200 shows test plans 202, original equipmentmanufacturer (OEM) lab 204, wireless network provider lab 206, KPI logs208, Extract-Transform-Load (ETL) module 210, analytics engine 31 andgraphical user interface module (GUI) module 214, field tester 216,supply chain logs 218 and data warehouse 220.

In some implementations, the framework 200 may be implemented by anorganization, such as a wireless network provider. In this example, thewireless network provider may operate the framework 200 to determine thequality of mobile devices that have been manufactured by certainmanufacturers for customers of the wireless network provider. In otherimplementations, the framework 200 may be implemented by any otherorganization or company to evaluate quality of any device. In otherwords, the framework 200 is not limited to wireless network providersand mobile devices. Furthermore, it is to be appreciated that one ormore components illustrated in FIG. 2 may be combined. Operationsperformed by one or more components may also be distributed across othercomponents.

With reference to FIG. 2 and in some implementations, test plans 202 mayinclude, but are not limited to, data and associated guidelines on howto test the mobile station 13 a (or any other device) for quality. Thetest plans may include machine-readable instructions as well ashuman-readable instructions. For example, the test plans may be read bya human tester or may be provided to a processor-based computer toperform one or more tests on the mobile station 13 a. Such a test may beperformed by a processor-based computer to determine certain operationalparameters and/or key performance indicators of the mobile station 13 a.In some examples, testing of the mobile station 13 a may be performed atOEM lab 204. The OEM lab 204 may be a testing facility that is operatedby a manufacturer of the mobile station 13 a. The OEM lab 204 may beoperated by one or more human testers and may include one or moretesting stations and testing computers. The wireless network providerlab 206 may be a testing facility similar to the OEM lab 204 but may beoperated by a wireless network provider that provides network servicesfor the mobile station 13 a. OEM lab 204 and wireless network providerlab 206 may be electronically connected to network 29 and/or 23 forcommunication with other components illustrated in FIG. 1. Simplystated, the general purpose of the OEM lab 204 and wireless networkprovider lab 206 can be to perform one or more tests or measurements onthe mobile station 13 a to determine operational parameters associatedwith the mobile station 13 a. It is to be appreciated that theimplementations are not limited to a single mobile station 13 a, but canoperate to test and evaluate any number of mobile stations in sequenceor in parallel.

In some implementations, KPI logs 208 may include, but are not limitedto, data from field tester 216 and OEM lab 204. KPI logs 208 can includeKPI data including, but not limited to, mean time between devicefailure, mean time to device repair, etc. and any other performanceparameter. The field tester 216 may test the mobile station 13 a inactual situations reflecting a real-world use of the mobile station 13a. KPI logs 208 may also include data from wireless network provider lab206. As discussed above, data from OEM lab 204 and the wireless networkprovider lab 206 can include, but are not limited to, operationalparameters (e.g., network connectivity, browser rendering quality,signaling parameters, etc.) associated with the mobile station 13 a. Insome implementations, the data from the KPI logs 208 and the supplychain logs 218 can be retrieved or extracted by the ETL module 210. TheETL module 210 may extract, transform and load transformed data intodata warehouse 220. Data transformations may include, but are notlimited to, re-formatting of the data into a common or open data format.

In some implementations, ETL module 210 may receive data as data filesin a particular data format (e.g., .drm file format). The ETL module 210may use a schema to extract data attributes from a .drm file, thenformat the data, transform the data, and finally load or store the datato the data warehouse 220. The data warehouse 220 may also includemetadata associated with the data received from the ETL module 210.Metadata can specify properties of data. In this way, the data warehouse220 may include, but is not limited to, transformed data field tester216, the OEM lab 204 and the wireless network provider lab 206. The datafrom the data warehouse 220 may be read by the analytics engine 31 toevaluate quality of the mobile station 13 a using the exemplary methodsdiscussed below. In some implementations, data from the data warehouse220 may be provided by the data warehouse 220 to the analytics engine31. The metadata in the data warehouse 220 can define data attributes aswell as their relations. The metadata may include two types of metadata;performance data attribute and a configuration data attribute.Performance data attributes may include, but are not limited to, deviceKPI name, device KPI unit, device KPI threshold (max and limit value),wireless network (RF) KPI name, RF KPI unit, RF KPI threshold (max andlimit value) etc. Configuration data attributes may include, but are notlimited to, device name, OEM name, device type, hardware configurationparameters, software parameters, sales data, returns data (per causecode), etc. Once data attributes are defined in a metadata file, theirrelations can be defined. FIG. 4 illustrates an exemplary interface todefine the relations between data attributes in the metadata. Theinterface can be an easy to use web-based interface. For example, a usermay use the project browser of the interface to select one or moreperformance data parameters (e.g., KPIs) and then use logical diagramsto configuration of mappings between both standard and proprietary dataformats. Furthermore, the interface can allow customizing conversion ofdata types. In addition, a visualization of derived mappings betweensource and target formats can also be provided.

In some implementations, the analytics engine 31 includes one or moreprocessors, storage and memory to process one or more algorithms andstatistical models to evaluate quality of the mobile station 13 a. Theanalytics engine 31 may train and mine the data from ETL module 210. Asan example, a training set can be a set of data used to discoverpotentially predictive relationships. Training sets are used inartificial intelligence, machine learning, genetic programming,intelligent systems, and statistics. A training set can be implementedto build an analytical model, while a test (or validation) set may beused to validate the analytical model that has been built. Data pointsin the training set may be excluded from the test (validation) set.Usually a dataset is divided into a training set and a validation set(and/or a ‘test set’) in several iterations when creating an analyticalmodel. In this way, for example, the analytics engine 31 may determinemodels to evaluate device quality. The analytics engine 31 can furtherprovide a report describing the evaluated device quality. The report mayinclude parameters considered in evaluating the device quality togetherwith an indication of the device quality relative to pre-determined (oracceptable) quality levels. For example, the report may include anindication of whether the quality of the device is above-par, sub-par oron-par relative to pre-determined (or acceptable) quality levels. Thereport may be displayed on a user interface of a computing device. Thereport may be used to modify aspects related to a supply chain or amanufacturing chain of the mobile station 13 a. For example, when it isdetermined that the network performance of the mobile station 13 a issub-par then manufacturer of the mobile station 13 a may take actionsneeded to change certain network components of the mobile station 13 aduring manufacturing or improve quality control for the networkcomponents. In another example, when it is determined that the browserperformance of the mobile station 13 a is sub-par then the manufacturermay take actions needed to change the browser of the mobile station 13 aduring software integration. Alternatively, the manufacturer may takeactions needed to improve browser testing during software integration.

In some implementations, the device quality evaluation report may beautomatically provided by the analytics engine 31 to manufacturing andsupply chain components to alter their processes and operations withouthuman intervention. In this scenario, the device quality evaluationreport may include or may be replaced by instructions from the analyticsengine 31 to alter operation of the manufacturing and supply chaincomponents. The analytics engine 31 may continually monitor and adjustmanufacturing and supply chain component operations based on evaluateddevice quality. For example, a device that programs firmware for networkcomponents on the mobile station 13 a may be instructed by the analyticsengine 31 to update firmware settings to improve sub-par networkperformance for the mobile station 13 a. Also, for example, a roboticarm integrating or soldering components on the mobile station 13 a maybe instructed by the analytics engine 31 to increase speed or rate ofoperation to meet a particular supply demand of the mobile station 13 a.In this way, the analytics engine 31 may increase efficiency ofmanufacturing and supply chain processes for the mobile station 13 a.

In some implementations, the device quality evaluation report may beautomatically transmitted by the analytics engine 31 to one or morecomputers operated at stores that sell/market the mobile station 13 a toend users. In this scenario, the device quality evaluation report mayinclude human readable instructions to update software on particulardevices, alter certain hardware components, prevent sale of certaindevices identified by their model number(s) and any otherinstructions/guidance related to devices sold by the stores. The devicequality evaluation report may be used by store employees to anticipateissues that may be associated with a particular manufactured batch ofthe mobile station 13 a. The store employees may also use the reports toissue device recall requests to end users so that known issues withdevices may be resolved before they are reported by the end-users. Insome implementations, a new device quality evaluation report may betransmitted by the analytics engine 31 to the one or more computers atpre-determined intervals (e.g., hourly, daily, weekly, monthly, etc.).

In some implementations, open interfaces (e.g., application programminginterfaces (APIs)) may be provided to vendors for reading/writing databetween the ETL module 210 and the analytics engine 31 and forvisualizing analytics results between the analytics engine 31 and GUI214. In some implementations, the wireless network provider may providea third-party vendor access to the analytics engine 31. In someimplementations, data may be processed incrementally by the analyticsengine 31 for instantaneous learning. Incremental learning is a machinelearning paradigm where a learning process takes place whenever newexample(s) emerge and adjusts what has been learned according to the newexample(s). Incremental learning differs from traditional machinelearning in that incremental learning may not assume the availability ofa sufficient training set before the learning process, but the trainingexamples appear over time. Based on this paradigm, the algorithmsutilized by the analytics engine 31 may be automatically updated byre-training the data processed by the analytics engine 31. In someimplementations, a dynamic sliding window method may be used to providedata from the ETL module 210 to the analytics engine 31 for algorithmtraining by the analytics engine 31. The dynamic sliding window may beused by the ETL module 210 to incrementally provide, for example,operational parameters from the mobile station 13 a to the analyticsengine 31. For example, and with reference to FIG. 3, the analyticsengine 31 may receive data incrementally from ETL module 210 and datawarehouse 220 as well as data from an KPI tool or KPI logs 208. Theanalytics engine 31 can incrementally auto-learn and update algorithms(and related formulae) for mathematical models so that the modelsconform to latest data received from the ETL module 210.

One or more outputs of the analytics engine 31 may be provided to theGUI 214 for display. As an example, the GUI 214 may rendered on a mobiledevice display, laptop display, personal computer display, etc. (e.g.,tablet computer, smartphone, etc.) that may display data provided by theanalytics engine 31. In some implementations, the GUI 214 can be used tovisualize analytical results from analytics engine 31. As an example,results from the analytics engine 31 may be visualized as terms ofcharts, animations, tables and any other form of graphical rendering.

In some implementations, GUI 214 may display a data model, algorithm(s),and inter-component communications. The GUI 214 may include a dashboardbased to support multiple applications while providing a unified lookand feel across all applications. The GUI 214 may display reports indifferent formats (e.g., .PDF, .XLS, etc.). The GUI 214 may allow openAPIs and objects for creation of custom report(s) by users. The GUI 214may keep the internal schema for the data warehouse 220 hidden fromusers and provide GUI features that include, but are not limited to,icons, grouping, icon extensions and administration menu(s). Overall,the GUI 124 can provide a consistent visual look and feel and userfriendly navigation.

In some implementations, the analytics engine 31 can be implemented as adevice analytics tool. Such a device analytics tool can be, for example,an extension of a Diagnostic Monitoring Tool to further evaluate devicequality for any pre-launched devices. Diagnostics can refer to adevice's self-diagnostic and reporting capability. Diagnostic or KPItools can give a device tester or repair technician access to status ofvarious device sub-systems. Diagnostic tools can use a standardizeddigital communications port to provide real-time data.

The analytics engine 31 can be a tool to apply statistical modelingalgorithms to study device quality, evaluate device readiness, andforecast device return rate etc. More statistical models can beembedded/stored in the analytics engine 31 based on business needs. Theanalytics engine 31 can allow a device quality team to investigatedevice quality from several aspects, including, but not limited to,device quality, device readiness and device return rate. For example,when evaluating device quality, a cliff model can be developed by theanalytics engine 31 to comprehensively evaluate device quality in termsof a device quality index which reflects user's experience with devicequality in high dimensional spaces. An exemplary, device qualityevaluation using a cliff model is discussed further below with respectto FIG. 8.

When evaluating device readiness, a sigmoid model can be developed bythe analytics engine 31 to forecast the time to market for a givenpre-launched device. When evaluating device return rate, a model such asa Local Weighted Scatter-Plot Smoothing (LOESS) model can developed bythe analytics engine 31 to forecast potential device return rate for agiven pre-launched device. This model can also be leveraged by theanalytics engine 31 to forecast device return rate for a post-launcheddevice.

In some implementations, the analytics engine 31 supports thefunctionality of instantaneous data extraction from an KPI module (e.g.,KPI logs 208) as well as instantaneous data transformation and loading.Instantaneous data processing in ETL module 210 can refer to access toOBDM data library (e.g., KPI logs 208) for data extraction. Dataobtained from the KPI logs 208 may be a quasi-instant step. The ETLmodule 210 can detect new data that is uploaded to KPI logs 208. The ETLmodule 210 can perform operations immediately once the new data file isdetected in KPI logs 208. Appropriate outlier exclusion approaches maybe implemented in the ETL module 210 to exclude and convert outliers, ordata that may not conform to known formats, in raw data files. In someimplementations, a wireless network provider may provide the outlierdetection algorithms to a vendor that provides OBDM tools to collectdata into KPI logs 208.

FIG. 5 illustrates different data sources of the analytics engine 31. Insome implementations, there can be three primary data sources to ETLmodule 210 and the analytics engine 31. These data sources include KPIlogs 208, supply chain database 502, and market forecast data 504. TheKPI logs 208 may be a primary data source to obtain performance data.There can be two data formats available in the KPI logs 208 for theanalytics engine 31. One format can be a .DRM format, which can be usedto store an original log file uploaded by KPI tool users to an KPI datalibrary. The other format can be a .CSV file, which can be used to storea report generated by an KPI tool. It can be viewed as a “processed log”via a KPI tool.

Supply chain database 502 can store data related to device rate ofreturn. In some implementations, the wireless network provider canprovide device returns data extracted from a logistics dashboardassociated with the supply chain database 502 in a .CSV file. Queriesand scripts may be run by ETL module 210 to extract raw data from thesupply chain database 502 and save the data as .CSV files. ETL module210 may accept and feed the data to the analytics engine 31 for furtherprocessing.

In implementations, market forecast data 504 can provide the sales dataper device per month. The market forecast data 504 can be obtained froma pre-existing database of forecasted sales data that may be generatedby a marketing team. This dataset can be merged with the device returnsdata from the supply chain database 502. Granularity of load-performancedata can be seconds, minutes, or busy hour intervals that may indicatewhen a device supply chain is active in tasks related to device issueresolution. Returns data and sales data may have granularity of weeks ormonths.

In some implementations, after data is processed by the ETL module 210but before it is passed to staging, a step of outlier exclusion may beimplemented by the ETL module 210. In some implementations, aninter-quartile range (IQR) algorithm may be implemented in the ETLmodule 210 to exclude outliers. In descriptive statistics, theinterquartile range, also called the midspread or middle fifty, is ameasure of statistical dispersion, being equal to the difference betweenthe upper and lower quartiles.

In some implementations, the ETL module 210 can define a unified targetfile. Each data attribute in metadata can be mapped by the ETL module210 to a given column in the target file. The target file is generatedas the output file by the ETL module 210 and then provided by the ETLmodule 210 to the analytics engine 31 for further data mining and dataprocessing. This target file can be utilized by the analytics engine forstatistical analysis (e.g., statistical analysis of the mobile station13 a). In some implementations, the ETL module 210 may need to split thetarget file into several files such as a performance file, aconfiguration file, etc. The target file may be split when the file sizeis larger than a specified threshold value. The performance file mayinclude data related to performance of hardware (e.g., memorycomponents) and software (e.g., executing applications) of the mobilestation 13 a. The configuration file may include data associated withcertain device settings (e.g., user defined settings) of the mobilestation 13 a.

As noted earlier, device quality can be considered to be a comprehensiveindex to reflect a user's experience with device quality in terms ofmany dimensions. These dimensions can include different areas of deviceperformance such as applications operating on the mobile station 13 a,hardware performance, software performance, network performance,signaling performance, etc. of the mobile station 13 a.

FIG. 6 is an exemplary relationship between DQI and service disturbance.The service disturbance can be, for example, associated with a webbrowser application that is being utilized by a user. Referring to FIG.6, region 1 illustrates a region of constant and optimal DQI. In region1, a slight growth of quality disturbance (0 to X1) may not affect DQIat all. As an example, a slight growth of quality disturbance may occurwhen the web browser displays content at a slower rate in comparison toa rate that is generally experienced by the user. Region 2 illustratesan area of sinking DQI. As an example, a slight growth of qualitydisturbance may occur when the web browser intermittently displayscontent at a much slower rate in comparison to a rate that is generallyexperienced by the user. Quasi-optimal device quality of experience(DQoE) may not be maintained when exceeding threshold X1. As a resultDQI, which corresponds to DQoE, sinks in terms of its negative gradient.Region 3 illustrates a region of unacceptable DQI. In region 3, deviceservice is unacceptably bad and the browser service may stop working. InFIG. 6, the shape of the DQI curve is given consideration by theanalytics engine 31. For example, it can be determined whether the shapeis linear, 2nd order polynomial, 3rd order polynomial, exponential,logarithmic, etc.

Device quality and key quality indicators (KQI) or analogous KPIs canpresent a relationship of exponential interdependency. By insertingmeasureable/computable device KQI/KPI values into an exponential formuladerived by the analytics engine 31, their impact on DQI can be assessedby the analytics engine 31. The sensitivity of the DQI can be morepronounced as the experienced quality increases. For example, if DQI isvery high, a small service disturbance may significantly decrease theDQI. Also, for example, if DQI is already low, a further servicedisturbance may not significantly decrease the DQI. As a result, it maybe determined that DQoE and KQI/KPIs represent a relationship ofexponential interdependency.

Furthermore, it may be assumed that change of DQI to device key qualityindex (DKQI) or quality of service (QoS)) may depend on the currentlevel of DQoE (e.g., alpha*DQoE), given a certain amount of change ofthe DKQI (or QoS) value (or beta). Then, based on example illustrationsfrom FIGS. 7A and 7B, it can be proved that DQI presents a negativeexponential relationship to service quality disturbance. At an initialtime, service disturbance (close to null point) can be very small. Forexample, jitter, latency, packet loss, etc. can be imperceptible andinvisible by device users. However, once the disturbance traverses aparticular threshold, user experience can plunge sharply.

Referring to FIG. 7A, region 702 illustrates a narrow variance band withstable and “good” device quality of experience relative to other qualityof experience values or parameters These “good” quality of experienceparameters may be specified as baseline (or acceptable) parameters by amanufacturer of the device. In region 702, the device KPIs/KQIs are verygood in comparison to regions 704, 706 and 708. At point 710, the DQoEstarts to degrade. Region 704 illustrates a wide variance band with“good” device quality of experience. In region 704, the device KPIs/KQIsare very good in comparison to the other regions 706 and 708. At point712, the DQoE starts to degrade significantly. Accordingly, region 706represents a region of poor DQoE. At point 714, KPI value becomesunacceptable and DQoE degrades into an unacceptable region representedby region 708.

Referring to FIG. 7A, when DQoE is represented by a monotonic decreasefunction with an inflection point, the 2^(nd) order derivative of theKPI disturbance can be represented by zero. Generally, the secondderivative measures how the rate of change of a quantity is itselfchanging. The point at which the second derivative changes sign can beconsidered to be the inflection point. In calculus, the first derivativecan indicate whether a function is increasing or decreasing, and by howmuch it is increasing or decreasing. The second derivative, or thesecond order derivative, of a function “f” is the derivative of thederivative of function f. Referring to FIG. 7B, when DQoE is representedby a non-monotonic decrease with a stationary point, the 1^(st) orderderivative of the KPI disturbance can be represented by zero. Then, ifthe 2^(nd) order derivative of the KPI disturbance is less than zero,then the stationary point represents a maximal extreme. However, if the2^(nd) order derivative of the KPI disturbance is greater than zero,then the stationary point represents a minimal extreme.

While the first derivative can tell us if the function is increasing ordecreasing, the second derivative tells us if the first derivative isincreasing or decreasing. If the second derivative is positive, then thefirst derivative is increasing, so that the slope of the tangent line tothe function is increasing. We see this phenomenon graphically as thecurve of the graph being concave up, that is, shaped like a parabolaopen upward. Likewise, if the second derivative is negative, then thefirst derivative is decreasing, so that the slope of the tangent line tothe function is decreasing. Graphically, we see this as the curve of thegraph being concave down, that is, shaped like a parabola open downward.

Assume the change of DQoE to DKQI (or QoS) depends on the current levelof DQoE (alpha*DQoE), given a certain amount of change of the DKQI (orQoS) value (beta).

Accordingly, we have:

$\begin{matrix}{\frac{{\partial D}\; Q\; o\; E}{{\partial D}\; K\; Q\; I} \sim {{{a \cdot D}\; Q\; o\; E} + \beta}} & (1) \\{\frac{{\partial D}\; Q\; o\; E}{{{a \cdot D}\; Q\; o\; E} + \beta} \sim {{\partial D}\; K\; Q\; I}} & (2) \\{{\int{{\frac{{\partial D}\; Q\; o\; E}{{{a \cdot D}\; Q\; o\; E} + \beta} \cdot {D}}\; Q\; o\; E}} \sim {\int{{\partial D}\; K\; Q\; {I \cdot {K}}\; Q\; I}}} & (3) \\{\left. \Rightarrow{D\; Q\; o\; E} \right. = {{A \cdot e^{{{- B} \cdot D}\; K\; Q\; I}} + C}} & (4)\end{matrix}$

Hence, based on the exemplary relationships noted above in equations (1)through (3), DQI, corresponding to DQoE, presents a negative exponentialrelationship to service quality disturbance as illustrated in equation(4). For example, at the beginning of a service disturbance (close to anull point) a drop in DQI may be small or imperceptible. Particularly,jitter, latency, packet loss etc. can imperceptible to device users.However, once the service disturbance increases beyond a particularthreshold, user experience will likely plunge significantly. Thisrelationship is captured by equation (4).

In some implementations, one or more operational parameters for mobilestation 13 a are received at the analytics engine 31. The operationalparameters can include data related to service disturbances in aplurality of areas of operational performance of the mobile station 13a. The analytics engine 31 can compute, for each dimension of aplurality of dimensions, a curve indicative of a relationship between adevice quality index (DQI) of the mobile station 13 a for a particulardimension and operational parameters of the mobile station 13 a for theparticular dimension. Each curve may be computed using exemplaryequation (4) noted above. Each dimension of the plurality of dimensionscan correspond to a particular area of operational performance of themobile station 13 a. Overall, the curves computed by the analyticsengine 31 represent an exponential multi-dimensional surface. Asdiscussed below, in the multi-dimensional surface, the analytics engine31 can identify the cliff jumping spot in each dimension where users'device quality experience may start to degrade.

The analytics engine 31 then identifies cliff points in each dimensionof a multi-dimensional surface fitted to each computed curve. Each cliffpoint corresponds to a reduction in a user's quality of experience in aparticular area of operational performance (e.g., network performance,browser performance, signalling performance, etc.) represented by acorresponding dimension. The reduction may be associated with theservice disturbances. Referring to FIG. 8, these service disturbancescan include, but are not limited to, application video disturbances, webbrowsing disturbances, network service quality disturbances, transportdisturbances and signaling disturbances. For example, axis 804represents an application video disturbance. Cliff point 806 is a DQIvalue at which the application video disturbance increasessignificantly. As a result, the DQI drops significantly after cliffpoint 806 resulting in a cliff-like feature. Similar cliff points areobserved for other areas of operational performance. For example, cliffpoint 808 is a DQI value at which the web browsing quality disturbanceincreases significantly. As a result, the DQI drops significantly aftercliff point 808 resulting in a cliff-like feature. The analytics engine31 then projects the identified cliff points in each dimension of themulti-dimensional surface to determine a horizontal space in themulti-dimensional surface corresponding to a region of acceptable DQIs.Thus, the analytics engine 31 expands a two-dimensional relation tomulti-dimensional spaces. In this way, the multi-dimensional surfacerepresents DQI as a comprehensive index to reflect users' device qualityexperience in many dimensions.

In some implementations, the analytics engine 31 can “cycle” or traversethe horizontal space 812 to be plotted by the projected points.Referring to FIG. 8, this horizontal space 812 can be considered to be a“safety zone.” As an example, as long as the measured KPI values of themobile station 13 a in each dimension stay in the scope of safety zonerepresented by the horizontal space 812, we can conclude that the DQI ofthe mobile station 13 a is acceptable. Zone 810 represents a set ofmeasured values by a given test. The zone 810 may be moveable. Forexample, in another test the analytics engine 31 may determine that themeasured zone 810 is moving towards signaling dimension. Such movementof the measured zone 810 may indicate that the signaling attributes maybe close to a quality threshold or a cliff jumping point beyond whichthe device quality of the mobile station 13 a may degrade. In someimplementations, this determination may be provided to the OEM of themobile station 13 a for further analysis.

The analytics engine 31 may receive a device quality index (DQI)associated with the mobile station 13 a and determine whether thereceived DQI is within the region of the acceptable DQIs. When thereceived DQI is determined to be the region of the acceptable DQIs, theanalytics engine 31 may determine that the mobile station 13 a is ofacceptable quality. When the received DQI is not determined to be theregion of the acceptable DQIs, the analytics engine 31 may determinethat the mobile station 13 a may not be of acceptable quality. In thisway, for example, the disclosed implementations may evaluate devicequality (e.g., quality of mobile device 13 a) and can further provide areport describing the evaluated device quality. The report may includeparameters considered in evaluating the device quality together with anindication of the device quality relative to pre-determined (oracceptable) quality levels. For example, the report may include anindication of whether the quality of the device is above-par, sub-par oron-par relative to pre-determined (or acceptable) quality levels. Thereport may be displayed on a user interface of a computing device. Thereport may be provided as a presentation, spreadsheet, text document, orin any other format. The report may also include animations toillustrate progress of a particular device through a manufacturingcycle. The report may highlight certain performance parameters of themobile station 13 a that may be greater or less than pre-definedbaseline parameters.

The disclosed implementations have several benefits. For example, theanalytics engine 31 jointly incorporates all the independent deviceperformance indicators in one single model to comprehensively evaluatedevice performance. The cliff model (e.g., the cliff model of FIG. 8 canbe used by the analytics engine 31 to quantitatively identify when andhow the device quality of a given device (e.g., the mobile station 13 a)starts to degrade. The disclosed implementations, determine a “safetyzone” (e.g., the zone 812) in a device quality model. The safety zonerepresents an area where the quality of a given device (e.g., the mobilestation 13 a) is acceptable. Cliff jumping spots computed by theanalytics engine 31 allow the analytics engine 31 to identify wheredevice quality starts to degrade. As a purely illustrative example,cliff jumping spots may be determined by the analytics engine 31 using2^(nd) partial differential equations. The analytics engine 31quantifies the relationship between device quality and deviceperformance indicators. The analytics engine 31 also addresses the jointrelations among all device performance indicators and how the jointrelation impacts device quality.

In this way, the disclosed implementations bridge a technology gap byleveraging a comprehensive analytical methodology and system to evaluatedevice performance and quality. The algorithms processed by theanalytics engine 31 form a unique and novel model to evaluate devicequality, which can be expanded to other fields such as network quality,service quality and any other quality testing domain. The analyticsengine 31 can compute generalized models which are universal todetermine solutions for any quality evaluation related problem. Wirelessnetwork provide teams can leverage the analytics engine to estimatedevice quality and maturity for all the pre-launched and post-launcheddevices from all the OEMs. The disclosed implementations provide for aneutralized model that is OEM agnostic to identify device quality andperformance and can be used to evaluate device quality for pre-launcheddevices as well as post-launch devices and further provide a reportdescribing the evaluated device quality

The enhanced device quality evaluation service under consideration heremay be used with touch screen type mobile stations as well as tonon-touch type mobile stations. Hence, our simple example shows themobile station (MS) 13 a as a non-touch type mobile station and showsthe mobile station (MS) 13 as a touch screen type mobile station.Implementation of the on-line device quality evaluation service mayinvolve at least some execution of programming in the mobile stations aswell as implementation of user input/output functions and datacommunications through the network 15, from the mobile stations.

Those skilled in the art presumably are familiar with the structure,programming and operations of the various type of mobile stations.However, for completeness, it may be useful to consider the functionalelements/aspects of two exemplary mobile stations 13 a and 13 b, at ahigh-level.

For purposes of such a discussion, FIG. 9 provides a block diagramillustration of an exemplary non-touch type mobile station 13 a.Although the mobile station 13 a may be a smart-phone or may beincorporated into another device, such as a personal digital assistant(PDA) or the like, for discussion purposes, the illustration shows themobile station 13 a is in the form of a handset. The handset embodimentof the mobile station 13 a functions as a normal digital wirelesstelephone station. For that function, the station 13 a includes amicrophone 102 for audio signal input and a speaker 104 for audio signaloutput. The microphone 102 and speaker 104 connect to voice coding anddecoding circuitry (vocoder) 106. For a voice telephone call, forexample, the vocoder 106 provides two-way conversion between analogaudio signals representing speech or other audio and digital samples ata compressed bit rate compatible with the digital protocol of wirelesstelephone network communications or voice over packet (InternetProtocol) communications.

For digital wireless communications, the handset 13 a also includes atleast one digital transceiver (XCVR) 108. Today, the handset 13 a wouldbe configured for digital wireless communications using one or more ofthe common network technology types. The concepts discussed hereencompass embodiments of the mobile station 13 a utilizing any digitaltransceivers that conform to current or future developed digitalwireless communication standards. The mobile station 13 a may also becapable of analog operation via a legacy network technology.

The transceiver 108 provides two-way wireless communication ofinformation, such as vocoded speech samples and/or digital information,in accordance with the technology of the network 15. The transceiver 108also sends and receives a variety of signaling messages in support ofthe various voice and data services provided via the mobile station 13 aand the communication network. Each transceiver 108 connects through RFsend and receive amplifiers (not separately shown) to an antenna 110.The transceiver may also support various types of mobile messagingservices, such as short message service (SMS), enhanced messagingservice (EMS) and/or multimedia messaging service (MMS).

The mobile station 13 a includes a display 118 for displaying messages,menus or the like, call related information dialed by the user, callingparty numbers, etc., including display of any information from theanalytics engine 31. A keypad 120 enables dialing digits for voiceand/or data calls as well as generating selection inputs, for example,as may be keyed-in by the user based on a displayed menu or as a cursorcontrol and selection of a highlighted item on a displayed screen. Thedisplay 118 and keypad 120 are the physical elements providing a textualor graphical user interface. Various combinations of the keypad 120,display 118, microphone 102 and speaker 104 may be used as the physicalinput output elements of the graphical user interface (GUI), formultimedia (e.g., audio and/or video) communications. Of course otheruser interface elements may be used, such as a trackball, as in sometypes of PDAs or smart phones.

In addition to normal telephone and data communication relatedinput/output (including message input and message display functions),the user interface elements also may be used for display of menus andother information to the user and user input of selections, includingany needed during operation of the analytics engine 31.

A microprocessor 112 serves as a programmable controller for the mobilestation 13 a, in that it controls all operations of the mobile station13 a in accord with programming that it executes, for all normaloperations, and for operations involved in the device quality evaluationprocedure under consideration here. In the example, the mobile station13 a includes flash type program memory 114, for storage of various“software” or “firmware” program routines and mobile configurationsettings, such as mobile directory number (MDN) and/or mobileidentification number (MIN), etc. The mobile station 13 a may alsoinclude a non-volatile random access memory (RAM) 116 for a working dataprocessing memory. Of course, other storage devices or configurationsmay be added to or substituted for those in the example. In a presentimplementation, the flash type program memory 114 stores firmware suchas a boot routine, device driver software, an operating system, callprocessing software and vocoder control software, and any of a widevariety of other applications, such as client browser software and shortmessage service software. The memories 114, 116 also store various data,such as telephone numbers and server addresses, downloaded data such asmultimedia content, and various data input by the user. Programmingstored in the flash type program memory 114, sometimes referred to as“firmware,” is loaded into and executed by the microprocessor 112.

As outlined above, the mobile station 13 a includes a processor, andprogramming stored in the flash memory 114 configures the processor sothat the mobile station is capable of performing various desiredfunctions, including in this case the functions involved in thetechnique for providing parameters needed for device quality evaluationby the analytics engine 31.

For purposes of such a discussion, FIG. 10 provides a block diagramillustration of an exemplary touch screen type mobile station 13 b.Although possible configured somewhat differently, at least logically, anumber of the elements of the exemplary touch screen type mobile station13 b are similar to the elements of mobile station 13 a, and areidentified by like reference numbers in FIG. 10. For example, the touchscreen type mobile station 13 b includes a microphone 102, speaker 104and vocoder 106, for audio input and output functions, much like in theearlier example. The mobile station 13 b also includes a at least onedigital transceiver (XCVR) 108, for digital wireless communications,although the handset 13 b may include an additional digital or analogtransceiver. The concepts discussed here encompass embodiments of themobile station 13 b utilizing any digital transceivers that conform tocurrent or future developed digital wireless communication standards. Asin the station 13 a, the transceiver 108 provides two-way wirelesscommunication of information, such as vocoded speech samples and/ordigital information, in accordance with the technology of the network15. The transceiver 108 also sends and receives a variety of signalingmessages in support of the various voice and data services provided viathe mobile station 13 b and the communication network. Each transceiver108 connects through RF send and receive amplifiers (not separatelyshown) to an antenna 110. The transceiver may also support various typesof mobile messaging services, such as short message service (SMS),enhanced messaging service (EMS) and/or multimedia messaging service(MMS).

As in the example of station 13 a, a microprocessor 112 serves as aprogrammable controller for the mobile station 13 b, in that it controlsall operations of the mobile station 13 b in accord with programmingthat it executes, for all normal operations, and for operations involvedin the device quality evaluation procedure under consideration here. Inthe example, the mobile station 13 b includes flash type program memory114, for storage of various program routines and mobile configurationsettings. The mobile station 13 b may also include a non-volatile randomaccess memory (RAM) 116 for a working data processing memory. Of course,other storage devices or configurations may be added to or substitutedfor those in the example. Hence, outlined above, the mobile station 13 bincludes a processor, and programming stored in the flash memory 114configures the processor so that the mobile station is capable ofperforming various desired functions, including in this case thefunctions involved in the technique for providing parameters needed fordevice quality evaluation by the analytics engine 31.

In the example of FIG. 10, the user interface elements included adisplay and a keypad. The mobile station 13 b may have a limited numberof key 130, but the user interface functions of the display and keypadare replaced by a touchscreen display arrangement. At a high level, atouchscreen display is a device that displays information to a user andcan detect occurrence and location of a touch on the area of thedisplay. The touch may be an actual touch of the display device with afinger, stylus or other object, although at least some touchscreens canalso sense when the object is in close proximity to the screen. Use of atouchscreen display as part of the user interface enables a user tointeract directly with the information presented on the display.

Hence, the exemplary mobile station 13 b includes a display 122, whichthe microprocessor 112 controls via a display driver 124, to presentvisible outputs to the device user. The mobile station 13 b alsoincludes a touch/position sensor 126. The sensor 126 is relativelytransparent, so that the user may view the information presented on thedisplay 122. A sense circuit 128 sensing signals from elements of thetouch/position sensor 126 and detects occurrence and position of eachtouch of the screen formed by the display 122 and sensor 126. The sensecircuit 128 provide touch position information to the microprocessor112, which can correlate that information to the information currentlydisplayed via the display 122, to determine the nature of user input viathe screen.

The display 122 and touch sensor 126 (and possibly one or more keys 130,if included) are the physical elements providing the textual andgraphical user interface for the mobile station 13 b. The microphone 102and speaker 104 may be used as additional user interface elements, foraudio input and output, including with respect to some device qualityevaluation related functions.

The structure and operation of the mobile stations 13 a and 13 b, asoutlined above, were described to by way of example, only.

As shown by the above discussion, functions relating to device qualityevaluation may be implemented on computers connected for datacommunication via the components of a packet data network, operating asthe analytics engine 31 as shown in FIG. 1. Although special purposedevices may be used, such devices also may be implemented using one ormore hardware platforms intended to represent a general class of dataprocessing device commonly used to run “server” programming so as toimplement the device quality evaluation functions discussed above,albeit with an appropriate network connection for data communication.

As known in the data processing and communications arts, ageneral-purpose computer typically comprises a central processor orother processing device, an internal communication bus, various types ofmemory or storage media (RAM, ROM, EEPROM, cache memory, disk drivesetc.) for code and data storage, and one or more network interface cardsor ports for communication purposes. The software functionalitiesinvolve programming, including executable code as well as associatedstored data, e.g. files used for device quality evaluation. The softwarecode is executable by the general-purpose computer that functions as theanalytics engine and/or that functions as a GUI rendering device. Inoperation, the code is stored within the general-purpose computerplatform. At other times, however, the software may be stored at otherlocations and/or transported for loading into the appropriategeneral-purpose computer system. Execution of such code by a processorof the computer platform enables the platform to implement themethodology for evaluating device quality, in essentially the mannerperformed in the implementations discussed and illustrated herein.

FIGS. 11 and 12 provide functional block diagram illustrations ofgeneral purpose computer hardware platforms. FIG. 11 is a network orhost computer platform, as may typically be used to implement a server.FIG. 12 depicts a computer with user interface elements, as may be usedto implement a personal computer or other type of work station orterminal device, although the computer of FIG. 12 may also act as aserver if appropriately programmed. It is believed that those skilled inthe art are familiar with the structure, programming and generaloperation of such computer equipment and as a result the drawings shouldbe self-explanatory.

A server, for example, includes a data communication interface forpacket data communication. The server also includes a central processingunit (CPU), in the form of one or more processors, for executing programinstructions. The server platform typically includes an internalcommunication bus, program storage and data storage for various datafiles to be processed and/or communicated by the server, although theserver often receives programming and data via network communications.The hardware elements, operating systems and programming languages ofsuch servers are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. Of course, theserver functions may be implemented in a distributed fashion on a numberof similar platforms, to distribute the processing load.

A computer type user terminal device, such as a PC or tablet computer,similarly includes a data communication interface CPU, main memory andone or more mass storage devices for storing user data and the variousexecutable programs (see FIG. 6). A mobile device type user terminal mayinclude similar elements, but will typically use smaller components thatalso require less power, to facilitate implementation in a portable formfactor. The various types of user terminal devices will also includevarious user input and output elements. A computer, for example, mayinclude a keyboard and a cursor control/selection device such as amouse, trackball, joystick or touchpad; and a display for visualoutputs. A microphone and speaker enable audio input and output. Somesmartphones include similar but smaller input and output elements.Tablets and other types of smartphones utilize touch sensitive displayscreens, instead of separate keyboard and cursor control elements. Thehardware elements, operating systems and programming languages of suchuser terminal devices also are conventional in nature, and it ispresumed that those skilled in the art are adequately familiartherewith.

Hence, aspects of the methods of evaluating device quality outlinedabove may be embodied in programming. Program aspects of the technologymay be thought of as “products” or “articles of manufacture” typicallyin the form of executable code and/or associated data that is carried onor embodied in a type of machine readable medium. “Storage” type mediainclude any or all of the tangible memory of the computers, processorsor the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide non-transitory storage at any time for the software programming.All or portions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer of the wireless network provider into thecomputer platform that will be the analytics engine. Thus, another typeof media that may bear the software elements includes optical,electrical and electromagnetic waves, such as used across physicalinterfaces between local devices, through wired and optical landlinenetworks and over various air-links. The physical elements that carrysuch waves, such as wired or wireless links, optical links or the like,also may be considered as media bearing the software. As used herein,unless restricted to non-transitory, tangible “storage” media, termssuch as computer or machine “readable medium” refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, such as may be used to implement the analyticsengine, etc. shown in the drawings. Volatile storage media includedynamic memory, such as main memory of such a computer platform.Tangible transmission media include coaxial cables; copper wire andfiber optics, including the wires that comprise a bus within a computersystem. Carrier-wave transmission media can take the form of electric orelectromagnetic signals, or acoustic or light waves such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer can read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to aprocessor for execution.

While the foregoing has described what are considered to be the bestmode and/or other examples, it is understood that various modificationsmay be made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that the teachings may beapplied in numerous applications, only some of which have been describedherein. It is intended by the following claims to claim any and allapplications, modifications and variations that fall within the truescope of the present teachings.

Unless otherwise stated, all measurements, values, ratings, positions,magnitudes, sizes, and other specifications that are set forth in thisspecification, including in the claims that follow, are approximate, notexact. They are intended to have a reasonable range that is consistentwith the functions to which they relate and with what is customary inthe art to which they pertain.

The scope of protection is limited solely by the claims that now follow.That scope is intended and should be interpreted to be as broad as isconsistent with the ordinary meaning of the language that is used in theclaims when interpreted in light of this specification and theprosecution history that follows and to encompass all structural andfunctional equivalents. Notwithstanding, none of the claims are intendedto embrace subject matter that fails to satisfy the requirement ofSections 101, 102, or 103 of the Patent Act, nor should they beinterpreted in such a way. Any unintended embracement of such subjectmatter is hereby disclaimed.

Except as stated immediately above, nothing that has been stated orillustrated is intended or should be interpreted to cause a dedicationof any component, step, feature, object, benefit, advantage, orequivalent to the public, regardless of whether it is or is not recitedin the claims.

It will be understood that the terms and expressions used herein havethe ordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”or any other variation thereof, are intended to cover a non-exclusiveinclusion, such that a process, method, article, or apparatus thatcomprises a list of elements does not include only those elements butmay include other elements not expressly listed or inherent to suchprocess, method, article, or apparatus. An element proceeded by “a” or“an” does not, without further constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises the element.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

What is claimed is:
 1. A method comprising: receiving, at an analyticsengine, one or more operational parameters for a mobile device, whereinthe operational parameters include data related to service disturbancesin a plurality of areas of operational performance of the mobile device;computing, at the analytics engine, for each dimension of a plurality ofdimensions, a curve indicative of a relationship between a devicequality index (DQI) of the mobile device for a particular dimension andoperational parameters of the mobile device for the particulardimension, wherein each dimension of the plurality of dimensionscorresponds to a particular area of operational performance of themobile device; identifying, at the analytics engine, cliff points ineach dimension of a multi-dimensional surface fitted to each computedcurve, wherein each cliff point corresponds to a reduction in a user'squality of experience in a particular area of operational performancerepresented by a corresponding dimension, the reduction associated withthe service disturbances; projecting, at the analytics engine, theidentified cliff points in each dimension of the multi-dimensionalsurface to determine a horizontal space in the multi-dimensional surfacecorresponding to a region of acceptable DQIs; and generating, based onthe projecting, a device quality evaluation report for the plurality ofareas of operational performance of the mobile device
 2. The method ofclaim 1, further comprising: receiving, at an analytics engine, a DQIassociated with the mobile device; determinining whether the receivedDQI is within the region of the acceptable DQIs; and when the receivedDQI is determined to be within the region of the acceptable DQIs,determining that the mobile device is of acceptable quality.
 3. Themethod of claim 1, further comprising: receiving, at an analyticsengine, one or more updated operational parameters for the mobiledevice, wherein the updated operational parameters include updated datarelated to service disturbances in the plurality of areas of performanceof the mobile device; and repeating the computing, the identifying andthe projecting with the updated data to determine an updated horizontalspace in the multi-dimensional surface corresponding to an updatedregion of acceptable DQIs.
 4. The method of claim 3, further comprising:comparing a position of the updated horizontal space to a position ofpreviously determined horizontal space to determine whether the updatedhorizontal space is located at a different position than the previouslydetermined horizontal space; and when the updated horizontal space islocated at the different position than the previously determinedhorizontal space, determining whether the different position is locatedcloser to a particular dimension relative to other dimensions of theplurality of dimensions.
 5. The method of claim 4, further comprising:when the position is located closer to the particular dimension relativeto the other dimensions, determining that the operational parametersassociated with the particular dimension relative to the otherdimensions are closer to a cliff point associated with the particulardimension.
 6. The method of claim 1, wherein the areas of performance ofthe mobile device include one or more of a hardware performance,software performance, network performance or signaling performance. 7.The method of claim 1, wherein the curve is a two-dimensional curve withDQIs representing a first dimension and operational parametersrepresenting a second dimension.
 8. An analytics engine comprising: acommunication interface configured to enable communication via a mobilenetwork; a processor coupled with the communication interface; a storagedevice accessible to the processor; and an executable program in thestorage device, wherein execution of the program by the processorconfigures the server to perform functions, including functions to:receive, at an analytics engine, one or more operational parameters fora mobile device, wherein the operational parameters include data relatedto service disturbances in a plurality of areas of operationalperformance of the mobile device; compute, at the analytics engine, foreach dimension of a plurality of dimensions, a curve indicative of arelationship between a device quality index (DQI) of the mobile devicefor a particular dimension and operational parameters of the mobiledevice for the particular dimension, wherein each dimension of theplurality of dimensions corresponds to a particular area of operationalperformance of the mobile device; identify, at the analytics engine,cliff points in each dimension of a multi-dimensional surface fitted toeach computed curve, wherein each cliff point corresponds to a reductionin a user's quality of experience in a particular area of operationalperformance represented by a corresponding dimension, the reductionassociated with the service disturbances; project, at the analyticsengine, the identified cliff points in each dimension of themulti-dimensional surface to determine a horizontal space in themulti-dimensional surface corresponding to a region of acceptable DQIs;and generate, based on the projection, a device quality evaluationreport for the plurality of areas of operational performance of themobile device
 9. The analytics engine of claim 8, wherein execution ofthe program by the processor configures the server to perform functions,including functions to: receive, at an analytics engine, DQI associatedwith the mobile device; determine whether the received DQI is within theregion of the acceptable DQIs; and when the received DQI is determinedto be within the region of the acceptable DQIs, determine that themobile device is of acceptable quality.
 10. The analytics engine ofclaim 8, wherein execution of the program by the processor configuresthe server to perform functions, including functions to: receive, at ananalytics engine, one or more updated operational parameters for themobile device, wherein the updated operational parameters includeupdated data related to service disturbances in the plurality of areasof performance of the mobile device; and repeat the computing, theidentifying and the projecting with the updated data to determine anupdated horizontal space in the multi-dimensional surface correspondingto an updated region of acceptable DQIs.
 11. The analytics engine ofclaim 10, wherein execution of the program by the processor configuresthe server to perform functions, including functions to: compare aposition of the updated horizontal space to a position of previouslydetermined horizontal space to determine whether the updated horizontalspace is located at a different position than the previously determinedhorizontal space; and when the updated horizontal space is located atthe different position than the previously determined horizontal space,determine whether the different position is located closer to aparticular dimension relative to other dimensions of the plurality ofdimensions.
 12. The analytics engine of claim 11, wherein execution ofthe program by the processor configures the server to perform functions,including functions to: when the position is located closer to theparticular dimension relative to the other dimensions, determine thatthe operational parameters associated with the particular dimensionrelative to the other dimensions are closer to a cliff point associatedwith the particular dimension.
 13. The analytics engine of claim 8,wherein the analytics engine incrementally learns operational parametersfrom the mobile device and optimizes one or more algorithms used by theanalytics engine.
 14. The analytics engine of claim 8, wherein the curveis a two-dimensional curve with DQIs representing a first dimension andoperational parameters representing a second dimension.
 15. Anon-transitory computer-readable medium comprising instructions which,when executed by one or more computers, cause the one or more computersto: receive, at an analytics engine, one or more operational parametersfor a mobile device, wherein the operational parameters include datarelated to service disturbances in a plurality of areas of operationalperformance of the mobile device; compute, at the analytics engine, foreach dimension of a plurality of dimensions, a curve indicative of arelationship between a device quality index (DQI) of the mobile devicefor a particular dimension and operational parameters of the mobiledevice for the particular dimension, wherein each dimension of theplurality of dimensions corresponds to a particular area of operationalperformance of the mobile device; identify, at the analytics engine,cliff points in each dimension of a multi-dimensional surface fitted toeach computed curve, wherein each cliff point corresponds to a reductionin a user's quality of experience in a particular area of operationalperformance represented by a corresponding dimension, the reductionassociated with the service disturbances; project, at the analyticsengine, the identified cliff points in each dimension of themulti-dimensional surface to determine a horizontal space in themulti-dimensional surface corresponding to a region of acceptable DQIs;and generate, based on the projection, a device quality evaluationreport for the plurality of areas of operational performance of themobile device
 16. The computer-readable medium of claim 15 furthercomprising instructions which, when executed by the one or morecomputers, cause the one or more computers to: receive, at an analyticsengine, a DQI associated with the mobile device; determine whether thereceived DQI is within the region of the acceptable DQIs; and when thereceived DQI is determined to be within the region of the acceptableDQIs, determine that the mobile device is of acceptable quality.
 17. Thecomputer-readable medium of claim 15 further comprising instructionswhich, when executed by the one or more computers, cause the one or morecomputers to: receive, at an analytics engine, one or more updatedoperational parameters for the mobile device, wherein the updatedoperational parameters include updated data related to servicedisturbances in the plurality of areas of performance of the mobiledevice; and repeat the computing, the identifying and the projectingwith the updated data to determine an updated horizontal space in themulti-dimensional surface corresponding to an updated region ofacceptable DQIs.
 18. The computer-readable medium of claim 17 furthercomprising instructions which, when executed by the one or morecomputers, cause the one or more computers to: compare a position of theupdated horizontal space to a position of previously determinedhorizontal space to determine whether the updated horizontal space islocated at a different position than the previously determinedhorizontal space; and when the updated horizontal space is located atthe different position than the previously determined horizontal space,determine whether the different position is located closer to aparticular dimension relative to other dimensions of the plurality ofdimensions.
 19. The computer-readable medium of claim 18 furthercomprising instructions which, when executed by the one or morecomputers, cause the one or more computers to: when the position islocated closer to the particular dimension relative to the otherdimensions, determine that the operational parameters associated withthe particular dimension relative to the other dimensions are closer toa cliff point associated with the particular dimension.
 20. The computerreadable medium of claim 15, wherein the areas of performance of themobile device include one or more of a hardware performance, softwareperformance, network performance or signaling performance.